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Rev. invest. clín ; 73(2): 120-126, Mar.-Apr. 2021. graf
Artigo em Inglês | LILACS | ID: biblio-1251872

RESUMO

ABSTRACT Background: Underestimation of the number of cases during the coronavirus disease 2019 (COVID-19) pandemic has been a constant concern worldwide. Detection of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) RNA using real-time reverse-transcription polymerase chain reaction (RT-PCR) is the most common method to confirm a case. However, these tests have suboptimal sensitivity. Objective: The objective of the study was to estimate the number of COVID-19 confirmed cases, intensive care unit (ICU) admissions and deaths in Mexico, accounting for the probabilities of false-negative tests. Methods: We used publicly available, national databases of all SARS-CoV-2 tests performed at public laboratories in Mexico between February 27 and October 31, 2020. We used the estimated probabilities of false-negative tests based on the day of clinical sample collection after symptom initiation calculated previously. With the resulting model, we estimated the corrected daily number of cases, ICU admissions, and deaths. Results: Among 2,024,822 people tested in Mexico between February 27 and October 31 with an available result, we estimated 1,248,583 (95% confidence interval 1,094,850-1,572,818) cases, compared to 902,343 cases reported with positive tests. ICU admissions and deaths were 15% and 8% higher than reported, respectively. Conclusion: Accounting for SARS-CoV-2 RT-PCR-based diagnostic testsҠprecision is a simple way to improve estimations for the true number of COVID-19 cases among tested persons.


Assuntos
Humanos , Teste para COVID-19/métodos , COVID-19/diagnóstico , Bases de Dados Factuais , Sensibilidade e Especificidade , Reação em Cadeia da Polimerase Via Transcriptase Reversa , Reações Falso-Negativas , Reação em Cadeia da Polimerase em Tempo Real/métodos , COVID-19/mortalidade , COVID-19/epidemiologia , Hospitalização/estatística & dados numéricos , Unidades de Terapia Intensiva/estatística & dados numéricos , México/epidemiologia
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